<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Does Economic Growth Matter? Technology-Push, Demand-Pull and Endogenous Drivers of Innovation in the Renewable Energy Industry

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Preprint , Report 01 Jan 2014 Germany Publisher:Elsevier BVJournal:SSRN Electronic Journal (eissn: 1556-5068,

Authors: Sam, Aflaki; Syed Abul, Basher; Andrea, Masini;

doi: 10.2139/ssrn.2549617

Does Economic Growth Matter? Technology-Push, Demand-Pull and Endogenous Drivers of Innovation in the Renewable Energy Industry

- Summary
- Subjects
- Metrics

Abstract

This paper aims to contribute to the longstanding technology-push vs. demand-pull debate and to the literature on renewable energy policy assessment. We argue that in addition to the traditional push–pull dichotomy, the drivers of technological change must be differentiated by whether they are exogenous or endogenous to the economic system and must be assessed with respect to their contribution to both the creation and the diffusion of innovation. We apply this perspective to study innovation in the renewable energy (RE) industry in 15 European Union countries from 1990 to 2012. Using different panel data estimators, we find that public R&D investments, policies supporting RE and per capita income all have a positive effect on either innovation creation or diffusion, whereas the variability of policy support has a negative impact on diffusion. However, impacts are heterogeneous and differ depending on the innovation dimension considered. Most importantly, we find that economic growth is a stronger driver of RE diffusion than technology-push or exogenous demand-pull mechanisms, whereas it is relatively ineffective at stimulating innovation creation. The effect of economic growth on RE diffusion exhibits a nonlinear, U-shaped pattern that resonates with the Environmental Kuznets Curve hypothesis. RE penetration remains negligible at low levels of growth whereas it increases sharply only after income per capita has reached a given threshold. This effect has both a direct cause (with increased affluence demand for environmental quality rises) and an indirect cause (with increased affluence expensive RE policies become more affordable and get implemented more extensively). Our findings have implications for policy making. They suggest that for RE diffusion to increase, innovation policies should be carefully balanced. Government action should be directed not only at shielding renewables from competition with fossil fuel technologies, but also at stimulating aggregated demand and economic growth.

Country

Germany

Related Organizations

HEC Paris
Qatar
East West University
Bangladesh
East West University
Bangladesh
HEC Paris
Qatar
Ludwig-Maximilians-Universität München
Germany

Keywords

Diffusion Processes, Nonstationary Panel, Deployment policy, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O38 - Government Policy, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O33 - Technological Change: Choices and Consequences • Diffusion Processes, Environmental Kuznets Curve, Renewable Energy, O31 - Innovation and Invention: Processes and Incentives, JEL: C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C23 - Panel Data Models • Spatio-temporal Models, Technological innovation, O44 - Environment and Growth, Spatio-temporal Models, O38 - Government Policy, C23 - Panel Data Models, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O4 - Economic Growth and Aggregate Productivity/O.O4.O44 - Environment and Growth, [SHS.GESTION]Humanities and Social Sciences/Business administration, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O31 - Innovation and Invention: Processes and Incentives, O33 - Technological Change: Choices and Consequences, jel: jel:O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O31 - Innovation and Invention: Processes and Incentives, jel: jel:O - Economic Development, Innovation, Technological Change, and Growth/O.O4 - Economic Growth and Aggregate Productivity/O.O4.O44 - Environment and Growth, jel: jel:C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C23 - Panel Data Models • Spatio-temporal Models, jel: jel:O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O38 - Government Policy, jel: jel:O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O33 - Technological Change: Choices and Consequences • Diffusion Processes

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	17
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average